# CutCI: Calculate confidence intervals for grouped values In maCorrPlot: Visualize artificial correlation in microarray data

## Description

`CutCI` groups values of one variable into intervals with the same number of observations each and computes confidence intervals for the mean of another variable in each interval.

`CIrho` computes the normal theory confidence interval for a vector of values.

## Usage

 ```1 2 3``` ```CutCI(dat, number = 10, func = mean, alpha=0.95) CIrho(rho, alpha = 0.95) ```

## Arguments

 `dat` a numerical data frame or matrix with two columns, the first of which gets averaged, and the second of which defines the grouping `number` the number of equal-count intervals `func` summary function for computing the mean `rho` a vector of measurements `alpha` the desired confidence level

## Details

The quantiles for the confidence interval are taken from the standard normal distribution, so a reasonable number of observations per interval would be good.

## Value

`CutCI` returns invisibly a list of length three:

 `x` the midpoints of the grouping intervals `y` the means within each interval, as computed by `func` `yci` a matrix with two columns, giving the lower and upper end of the confidence interval respectively

`CIrho` returns a vector of length two, containing the lower and upper end of the confidence interval.

## See Also

`co.intervals`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```x = rnorm(100, mean=2) CIrho(x) y = 2 + 3*x + rnorm(100) cc = CutCI(cbind(x,y), number=5) print(cc) # Show it plot(cc\$x, cc\$y) arrows(cc\$x, cc\$yci[,1], cc\$x, cc\$yci[,2], length=0) ```

maCorrPlot documentation built on May 2, 2018, 2:59 a.m.